About

Dedicated to improving quality of life by enhancing the functionality of artificial hands and their control in human-machine systems.

 

To this end, we study:

  • Neuromuscular biomechanics of human grasp to elucidate patterns of reflex-like grip responses that can be used as inspiration for low-level reflexes in artificial hands
  • Neuromuscular control of multiple digits during manipulation tasks
  • Tactile sensor technology that can provide rich tactile feedback for use in real-time control of artificial fingertips
  • Machine-learning algorithms that will enable the mapping of tactile sensor signals to features of finger-object interactions
  • Reinforcement learning approaches to enable robots to learn how to perform hard-to-code tasks through experience
  • Control and sensory challenges for human-machine systems
  • Reducing cognitive burden via sensory-event driven, low-level reflex algorithms. Such artificial reflexes could serve as “survival instincts” which buy time for human operators of robotic devices to detect, process, and command a response to perturbations.

 

Our research is intended to advance the design and control of human-machine systems as well as autonomous robotic systems.

Example applications include:

  • Prosthetic hands for improving the independence and quality of life of amputees
  • Wheelchair-mounted robot hand and arm for increasing the workspace, independence, and quality of life of wheelchair users
  • Semi-autonomous, teleoperated manipulators for use in harsh or limited-access environments, such as for nuclear power plant decommissioning or explosive ordnance disposal